
"""simple docstring"""



import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock

import torch
from accelerate.utils import write_basic_config

from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available


logging.basicConfig(level=logging.DEBUG)

a       :						str       		=		logging.getLogger()


def 			_UpperCamelCase						(    )     ->	str:





    """simple docstring"""

    _UpperCAmelCase        = argparse.ArgumentParser()
    parser.add_argument("""-f"""   )
    _UpperCAmelCase        = parser.parse_args()
    return args.f


def 			_UpperCamelCase						(    _A   )     ->	List[Any]:





    """simple docstring"""

    _UpperCAmelCase        = {}
    _UpperCAmelCase        = os.path.join(_A       ,   """all_results.json"""   )
    if os.path.exists(_A   ):
        with open(_A       ,   """r"""   ) as f:
            _UpperCAmelCase        = json.load(_A   )
    else:
        raise ValueError(F"""can't find {path}"""   )
    return results


def 			_UpperCamelCase						(    )     ->	Dict:





    """simple docstring"""

    _UpperCAmelCase        = torch.cuda.is_available() and torch_device == """cuda"""
    return is_using_cuda and is_apex_available()


a       :						Tuple       		=		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class 		a_    (			_UpperCAmelCase						):







    @classmethod
    def   _snake_case       (      cls      :							Any  )		->str:






        '''simple docstring'''






        _UpperCAmelCase        = tempfile.mkdtemp()
        _UpperCAmelCase        = os.path.join(cls.tmpdir  ,       """default_config.yml"""  )
        write_basic_config(save_location=cls.configPath  )
        _UpperCAmelCase        = ["""accelerate""", """launch""", """--config_file""", cls.configPath]







    @classmethod
    def   _snake_case       (      cls      :							Optional[int]  )		->List[Any]:






        '''simple docstring'''






        shutil.rmtree(cls.tmpdir  )







    @mock.patch.dict(os.environ  ,       {"""WANDB_MODE""": """offline"""}  )
    def   _snake_case       (      self      :							List[str]  )		->str:






        '''simple docstring'''






        _UpperCAmelCase        = self.get_auto_remove_tmp_dir()
        _UpperCAmelCase        = f"""
            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --seed=42
            --checkpointing_steps epoch
            --with_tracking
        """.split()

        if is_cuda_and_apex_available():
            testargs.append("""--fp16"""  )

        run_command(self._launch_args + testargs  )
        _UpperCAmelCase        = get_results(__UpperCamelCase  )
        self.assertGreaterEqual(result["""eval_accuracy"""]  ,       0.7_5  )
        self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase  ,       """epoch_0"""  )  )  )
        self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase  ,       """glue_no_trainer"""  )  )  )







    @mock.patch.dict(os.environ  ,       {"""WANDB_MODE""": """offline"""}  )
    def   _snake_case       (      self      :							List[Any]  )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase        = self.get_auto_remove_tmp_dir()
        _UpperCAmelCase        = f"""
            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --block_size 128
            --per_device_train_batch_size 5
            --per_device_eval_batch_size 5
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --checkpointing_steps epoch
            --with_tracking
        """.split()

        if torch.cuda.device_count() > 1:
            # Skipping because there are not enough batches to train the model + would need a drop_last to work.
            return

        run_command(self._launch_args + testargs  )
        _UpperCAmelCase        = get_results(__UpperCamelCase  )
        self.assertLess(result["""perplexity"""]  ,       1_00  )
        self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase  ,       """epoch_0"""  )  )  )
        self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase  ,       """clm_no_trainer"""  )  )  )







    @mock.patch.dict(os.environ  ,       {"""WANDB_MODE""": """offline"""}  )
    def   _snake_case       (      self      :							Dict  )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase        = self.get_auto_remove_tmp_dir()
        _UpperCAmelCase        = f"""
            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --num_train_epochs=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

        run_command(self._launch_args + testargs  )
        _UpperCAmelCase        = get_results(__UpperCamelCase  )
        self.assertLess(result["""perplexity"""]  ,       42  )
        self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase  ,       """epoch_0"""  )  )  )
        self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase  ,       """mlm_no_trainer"""  )  )  )







    @mock.patch.dict(os.environ  ,       {"""WANDB_MODE""": """offline"""}  )
    def   _snake_case       (      self      :							List[Any]  )		->Any:






        '''simple docstring'''






        _UpperCAmelCase        = 7 if get_gpu_count() > 1 else 2

        _UpperCAmelCase        = self.get_auto_remove_tmp_dir()
        _UpperCAmelCase        = f"""
            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
            --checkpointing_steps epoch
            --with_tracking
        """.split()

        run_command(self._launch_args + testargs  )
        _UpperCAmelCase        = get_results(__UpperCamelCase  )
        self.assertGreaterEqual(result["""eval_accuracy"""]  ,       0.7_5  )
        self.assertLess(result["""train_loss"""]  ,       0.5  )
        self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase  ,       """epoch_0"""  )  )  )
        self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase  ,       """ner_no_trainer"""  )  )  )







    @unittest.skip(reason="""Fix me @muellerzr"""  )
    @mock.patch.dict(os.environ  ,       {"""WANDB_MODE""": """offline"""}  )
    def   _snake_case       (      self      :							List[Any]  )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase        = self.get_auto_remove_tmp_dir()
        _UpperCAmelCase        = f"""
            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --seed=42
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

        run_command(self._launch_args + testargs  )
        _UpperCAmelCase        = get_results(__UpperCamelCase  )
        # Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
        self.assertGreaterEqual(result["""eval_f1"""]  ,       28  )
        self.assertGreaterEqual(result["""eval_exact"""]  ,       28  )
        self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase  ,       """epoch_0"""  )  )  )
        self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase  ,       """qa_no_trainer"""  )  )  )







    @mock.patch.dict(os.environ  ,       {"""WANDB_MODE""": """offline"""}  )
    def   _snake_case       (      self      :							List[Any]  )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase        = self.get_auto_remove_tmp_dir()
        _UpperCAmelCase        = f"""
            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/swag/sample.json
            --validation_file tests/fixtures/tests_samples/swag/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=20
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --with_tracking
        """.split()

        run_command(self._launch_args + testargs  )
        _UpperCAmelCase        = get_results(__UpperCamelCase  )
        self.assertGreaterEqual(result["""eval_accuracy"""]  ,       0.8  )
        self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase  ,       """swag_no_trainer"""  )  )  )







    @slow
    @mock.patch.dict(os.environ  ,       {"""WANDB_MODE""": """offline"""}  )
    def   _snake_case       (      self      :							Any  )		->Any:






        '''simple docstring'''






        _UpperCAmelCase        = self.get_auto_remove_tmp_dir()
        _UpperCAmelCase        = f"""
            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

        run_command(self._launch_args + testargs  )
        _UpperCAmelCase        = get_results(__UpperCamelCase  )
        self.assertGreaterEqual(result["""eval_rouge1"""]  ,       10  )
        self.assertGreaterEqual(result["""eval_rouge2"""]  ,       2  )
        self.assertGreaterEqual(result["""eval_rougeL"""]  ,       7  )
        self.assertGreaterEqual(result["""eval_rougeLsum"""]  ,       7  )
        self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase  ,       """epoch_0"""  )  )  )
        self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase  ,       """summarization_no_trainer"""  )  )  )







    @slow
    @mock.patch.dict(os.environ  ,       {"""WANDB_MODE""": """offline"""}  )
    def   _snake_case       (      self      :							Tuple  )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase        = self.get_auto_remove_tmp_dir()
        _UpperCAmelCase        = f"""
            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py
            --model_name_or_path sshleifer/student_marian_en_ro_6_1
            --source_lang en
            --target_lang ro
            --train_file tests/fixtures/tests_samples/wmt16/sample.json
            --validation_file tests/fixtures/tests_samples/wmt16/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --num_beams=6
            --learning_rate=3e-3
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
            --checkpointing_steps epoch
            --with_tracking
        """.split()

        run_command(self._launch_args + testargs  )
        _UpperCAmelCase        = get_results(__UpperCamelCase  )
        self.assertGreaterEqual(result["""eval_bleu"""]  ,       30  )
        self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase  ,       """epoch_0"""  )  )  )
        self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase  ,       """translation_no_trainer"""  )  )  )







    @slow
    def   _snake_case       (      self      :							List[Any]  )		->int:






        '''simple docstring'''






        _UpperCAmelCase        = logging.StreamHandler(sys.stdout  )
        logger.addHandler(__UpperCamelCase  )

        _UpperCAmelCase        = self.get_auto_remove_tmp_dir()
        _UpperCAmelCase        = f"""
            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
            --dataset_name huggingface/semantic-segmentation-test-sample
            --output_dir {tmp_dir}
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
        """.split()

        run_command(self._launch_args + testargs  )
        _UpperCAmelCase        = get_results(__UpperCamelCase  )
        self.assertGreaterEqual(result["""eval_overall_accuracy"""]  ,       0.1_0  )







    @mock.patch.dict(os.environ  ,       {"""WANDB_MODE""": """offline"""}  )
    def   _snake_case       (      self      :							Optional[int]  )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase        = self.get_auto_remove_tmp_dir()
        _UpperCAmelCase        = f"""
            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
            --model_name_or_path google/vit-base-patch16-224-in21k
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --max_train_steps 2
            --train_val_split 0.1
            --seed 42
            --output_dir {tmp_dir}
            --with_tracking
            --checkpointing_steps 1
        """.split()

        if is_cuda_and_apex_available():
            testargs.append("""--fp16"""  )

        run_command(self._launch_args + testargs  )
        _UpperCAmelCase        = get_results(__UpperCamelCase  )
        # The base model scores a 25%
        self.assertGreaterEqual(result["""eval_accuracy"""]  ,       0.6  )
        self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase  ,       """step_1"""  )  )  )
        self.assertTrue(os.path.exists(os.path.join(__UpperCamelCase  ,       """image_classification_no_trainer"""  )  )  )

"""simple docstring"""



import math
import unittest


def 			_UpperCamelCase						(    _A   )     ->	bool:





    """simple docstring"""

    assert isinstance(_A       ,   _A   ) and (
        number >= 0
    ), "'number' must been an int and positive"

    if 1 < number < 4:
        # 2 and 3 are primes
        return True
    elif number < 2 or number % 2 == 0 or number % 3 == 0:
        # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
        return False

    # All primes number are in format of 6k +/- 1
    for i in range(5       ,   int(math.sqrt(_A   ) + 1   )       ,   6   ):
        if number % i == 0 or number % (i + 2) == 0:
            return False
    return True




class 		a_    (			unittest.TestCase						):







    def   _snake_case       (      self      :							int  )		->Optional[int]:






        '''simple docstring'''






        self.assertTrue(is_prime(2  )  )
        self.assertTrue(is_prime(3  )  )
        self.assertTrue(is_prime(5  )  )
        self.assertTrue(is_prime(7  )  )
        self.assertTrue(is_prime(11  )  )
        self.assertTrue(is_prime(13  )  )
        self.assertTrue(is_prime(17  )  )
        self.assertTrue(is_prime(19  )  )
        self.assertTrue(is_prime(23  )  )
        self.assertTrue(is_prime(29  )  )







    def   _snake_case       (      self      :							Union[str, Any]  )		->Optional[Any]:






        '''simple docstring'''






        with self.assertRaises(__UpperCamelCase  ):
            is_prime(-19  )
        self.assertFalse(
            is_prime(0  )  ,       """Zero doesn't have any positive factors, primes must have exactly two."""  ,       )
        self.assertFalse(
            is_prime(1  )  ,       """One only has 1 positive factor, primes must have exactly two."""  ,       )
        self.assertFalse(is_prime(2 * 2  )  )
        self.assertFalse(is_prime(2 * 3  )  )
        self.assertFalse(is_prime(3 * 3  )  )
        self.assertFalse(is_prime(3 * 5  )  )
        self.assertFalse(is_prime(3 * 5 * 7  )  )


if __name__ == "__main__":
 unittest.main()